{"id":"W6983475622","doi":"","title":"Mitigating the Cold-Start Problem by Leveraging Category Level Associations","year":2023,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mitacs; Concordia University","keywords":"Recommender system; Collaborative filtering; Pairwise comparison; Quality (philosophy); Association (psychology); Range (aeronautics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008580474,0.001608752,0.005179153,0.003508925,0.002172982,0.003015827,0.005468147,0.003350992,0.001612887],"category_scores_gemma":[0.02263791,0.001690543,0.002214627,0.005669387,0.00136171,0.007642875,0.003906495,0.004813632,0.002589528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007664856,"about_ca_system_score_gemma":0.002378007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122534,"about_ca_topic_score_gemma":0.0327067,"domain_scores_codex":[0.9912142,0.002475952,0.000584262,0.002146588,0.003046725,0.0005322327],"domain_scores_gemma":[0.9554217,0.02006912,0.002929896,0.01198091,0.008690709,0.0009077244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001504303,0.002247079,0.08817594,0.001184632,0.002186526,0.001058841,0.003544376,0.1153035,0.03529581,0.0146558,0.02019532,0.7146477],"study_design_scores_gemma":[0.00006488866,0.0006246077,0.01586694,0.0001128376,0.0004608824,0.001137559,0.0005567081,0.9429817,0.01252511,0.01605137,0.009398263,0.0002191131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09334229,0.002046071,0.897737,0.000532769,0.0002134938,0.0002739477,0.0005052976,0.002086908,0.003262205],"genre_scores_gemma":[0.5969941,0.000995625,0.3888732,0.0008375755,0.0004203942,0.0002992486,0.002290496,0.000362878,0.008926338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01122534,"threshold_uncertainty_score":0.04537845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06022931303540516,"score_gpt":0.2929599561911984,"score_spread":0.2327306431557932,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}